text change

ML Notes.md

@@ -1,6 +1,7 @@
 ## Basic hypothesis (model):
 
 $$ h_\theta(x) = \theta_o x_0 + \theta_1 x_1 + \theta_2 x_2....\theta_n x_n  $$
+
 where $\theta_n$ is a parameter value to be calculated and $x_n$ is the value for the feature in the sample (training) data (with n features)
 
 Basic cost function:
@@ -25,8 +26,10 @@ Gradient descent uses partial derivatives with respect to each $\theta$ value, o
 
 for each iteration, calculate new $\theta$ values:
 
+
 $$    \theta_0 = \theta_0 - \alpha\frac{1}{m}\sum_{i=1}^m(h_\theta(x^{i}) - y^{i})*x_0^{(i)}) $$
 $$    \theta_1 = \theta_1 - \alpha\frac{1}{m}\sum_{i=1}^m(h_\theta(x^{i}) - y^{i})*x_1^{(i)}) $$
+
 ...
 
 $$    \theta_n = \theta_n - \alpha\frac{1}{m}\sum_{i=1}^m(h_\theta(x^{i}) - y^{i})*x_n^{(i)}) $$